In planning for retirement, it is important to project how many assets and retirement income sources a person needs to accumulate to sustain their needs and lifestyle goals through retirement. It is also important to project the approximate retirement budget that one's present assets and anticipated future assets should be able to sustain.
Financial advisors typically advise their clients that in order to prepare for retirement, they need to invest a large percentage of their assets in the stock market. They also typically recommend that their clients allocate a large percentage of their assets to stocks during retirement. The anticipated rewards of being invested in stocks, as compared to other financial instruments, are generally regarded as too great to pass up.
At the same time, financial advisors typically warn retirees that because of significant stock market volatility, they should limit their withdrawals. In 2008—after the date of the present invention—Nobel price recipient Prof. William F. Sharpe co-authored an article entitled “The 4% Rule—At What Price?” which observes that “[t]he 4% rule is the advice most often given to retirees for managing spending and investing.” The authors remarked that they were “struck by the universal popularity of the 4% rule—retail brokerage firms, mutual fund companies, retirement groups, investor groups, financial websites, and the popular financial press all recommend it,” and observed this platitude “is the most endorsed, publicized, and parroted piece of advice that a retiree is likely to hear.”
Many studies support the 4% rule. The volatility and associated risks of being significantly invested in stocks are, in fact, so great that retirees determined to maintain a constant level of inflation-adjusted spending must minimize the initial percentage they take from their portfolio to ensure that their assets last their lifetimes.
Although significant data supports the 4% rule, an almost universally missed consequence of the rule (with a few notable exceptions, such as Sharpe's article above) is that retirees are directed to withdraw less from a portfolio invested in the stock market than they could sustainably withdraw, with inflation adjustments, from a portfolio that was fully invested in relatively risk-free assets, like treasury inflation-protected securities (“TIPS”). The 4% rule is better tailored to helping the retiree live poor and die rich than to live rich. Except for retirees who wish to maximize what they leave behind to heirs, this approach to retirement investing and spending makes little sense. Yet this contradiction is seldom, if ever, recognized by either the advisors offering the advice, or the retirees receiving it, because few, if any, ever bother to compute a sustainable inflation-adjusted withdrawal amount from an all-TIPS portfolio or other suitable proxy for an approximately risk-free portfolio.
Publicly available advice, analyses, and resources on investing and retirement planning also overestimate the expected return on an equity portfolio. Scores of Monte Carlo and historical simulators purport to model the growth of an equity portfolio divided between generally recognized asset classes. This includes most of the leading financial planning software suites marketed to and used extensively by professionals. In general, these tools use historical return rates, by default, to project future return rates. Their Monte Carlo engines also typically model returns using a stationary normal or log normal distribution. The modeling assumptions of these simulators are significantly flawed.
First, most Monte Carlo and historical simulators use historical rates of return, rather than any mathematically or economically sound analysis of future returns (such as the dividend discount model) to project future rates of return. The assumption that future return rates will be as generous as historical return rates is a fundamentally unsound one. Many economists have decomposed historical returns into constituent components, such as the average dividend yield, an expansion in price-to-earnings ratios (which cannot continue indefinitely), and growth in corporate earnings. But when projecting future returns from the same constituents, we are forced to start with a lower dividend yield and little hope of continuing expansion in price-to-earnings ratios. Historical return rates, therefore, serve as an excessively optimistic proxy for the expected long term return, going forward, on broad stock market indices. Nevertheless, reliance on historical return rates to project future return rates is pervasive and practically universal.
Remarkably, Congress recently passed legislation strongly deterring financial planning software makers from ever departing from this flawed historical-returns approach. The Pension Protection Act of 2006 provides exemptive relief for a certified computer-program-based investment advice program from certain prohibited transaction provisions. To qualify, section 408(g)(3) requires that the computer model “appl[y] generally accepted investment theories that take into account the historic returns of different asset classes over defined periods of time” and “utilizes prescribed objective criteria to provide asset allocation portfolios comprised of investment options available under the plan.”
In 2006, the Department of Labor, pursuant to that act, issued a Request for Information regarding such computer models. In 2007, the Securities Industry and Financial Markets Association (SIFMA) responded to that request for information. SIFMA submitted—without qualification—that computer “[m]odels are based on mean variance optimization, and the program uses expected volatility and expected return, based on historical performance, to provide asset allocation and investment product results.” Moreover, “[t]he process used for designing the model is quite straightforward. The model is loaded with risk and return characteristics for each asset class represented in the plan, based on publicly available information.”
Until the flaws of the conventional approach to modeling equity returns are widely recognized among persons of ordinary skill, and both the industry and the law are transformed by that recognition, millions of 401(k) participants and financial advisory clients will continue to have their expectations unrealistically raised, followed by almost inevitable disappointment and cynicism.
Second, modeling returns using a stationary normal or log normal distribution potentially exaggerates the dispersion of multi-period outcomes—that is, outcomes over long timeframes, such as an investor's remaining life expectancy. Modeling returns with a stationary normal or log normal distribution assumes that there is no serial correlation between returns. Consequently, the variance in projections of accumulated wealth will be roughly proportional with the number of years over which the simulation is performed. This results in a very dramatic dispersion of simulated outcomes over long time frames. But historical experience does not support this model. In their July, 1987 NBER Working Paper entitled “Mean Reversion in Stock Prices: Evidence and Implications,” authors James Poterba and Lawrence Summers developed evidence that stock returns are positively serially correlated over short periods and negatively autocorrelated over long periods.
The teachings of such financial literature has apparently escaped the grasp of the persons of ordinary skill developing the leading computerized financial planning systems. For their Monte Carlo simulation models continue—many years after these papers were published—to model equity asset classes with stationary Gaussian or log normal distributions centered about historical return rates. As a consequence, these software packages generate reports for millions of financial advisor clients that have hopelessly optimistic median forecasts within an absurdly broad range of forecast outcomes. A more realistic model of future outcomes would populate the low end of the distribution of popularly forecast outcomes.
And while persons of ordinary skill in the art have recently begun to recognize, with the Panic of 2008, that their models are flawed, they apparently do not understand how they are flawed. Instead of recognizing and acknowledging that their models use stationary distributions when a non-stationary distribution would be more realistic and empirically supportable, or that their stationary distributions were centered about too high an expected return, there has been a pervasive assumption that their distributions simply do not exhibit fat enough tails. Accordingly, since the 2008 financial crisis, the major financial planning software vendors have focused on addressing the so-called “fat-tail” problem with their distributions.
Third, the category of inflation-protected bonds is not a “generally recognized asset class.” Consequently, very few simulators model the performance of such assets. Moreover, publicly available advice, analyses, and resources on investing and retirement planning rarely take into account the sustainable annual retirement budget an all-TIPS portfolio could sustain. Applicant is unaware of any retirement calculators and Monte Carlo simulators, created by others, that not only compute or estimate a “safe withdrawal rate” or amount, the “shortfall risk,” or the “survival risk” of a mixed portfolio, but also compare those results with the rewards (in terms of sustainable inflation-adjusted retirement budgets) and risk (in terms of shortfall risk) of a minimum risk (e.g., all-TIPS) portfolio. Applicant is also unaware of any retirement calculators or Monte Carlo simulators, created by others, that evaluate the risk of a mixed portfolio, or the dispersion of simulated returns of a mixed portfolio, in relation to the expected performance a minimum risk portfolio.
Incidentally, there is no motivation to even consider and compare the sustainable annual retirement budget an all-TIPS portfolio could support if one assumes that the large equity risk premiums of the past 100 or so years will persist into the future. The fixation with historical returns and assumption that they will persist may explain, in part, why most persons of ordinary skill in the art have been so disinterested in promoting inflation-protected bonds to a generally recognized asset class for individuals preparing for retirement.
There is also a significant regulatory deterrent to making such a comparison. Paragraph (d)(1) of a proposed Department of Labor regulation implementing the Pension Protection Act of 2006 would prohibit a qualifying computer model from “giving inappropriate weight to any investment option.” 73 Fed. Reg. 49895, 49899 (Aug. 22, 2008). This regulation would, in effect, prohibit a qualifying computer program from comparing the risks and rewards of a mixed portfolio with an all-TIPS portfolio. Applicant has found no commentary discussing this potential consequence of the rule, which only reinforces Applicant's contention that it is not obvious to persons of ordinary skill to project equity portfolio performance in terms of how it would compare to the performance of an all-TIPS portfolio.
Applicant has reviewed documentation for several leading financial planning software suites marketed to and used extensively by professionals. The leading provider is Emerging Information Systems Inc. (EISI) of Winnipeg, Manitoba, Canada, which markets its NaviPlan and Financial Profiles software to over 250,000 financial professionals. As of 2006, a distant second-place competitor, Financeware, Inc., of Richmond, Va., reported having over 28,000 users of its Financeware wealth management software. As of 2006, another competitor, PIETech of Powhatan, Va., reported having over 17,000 users of its Money Guide Pro software.
EISI's market-leading NaviPlan software, as of 2008, uses a stationary normal distribution centered about average historical returns to project future equity return rates. As late as mid-2008, NaviPlan's software projected long-term annual return rates of between 10% and 15%/year in six different generally recognized asset class categories. Assuming an inflation rate of about 3% a year, the economy would have to grow, in the long term, and in inflation-adjusted terms, well in excess of 5% per year and perhaps as high as 10% per year to support NaviPlan's equity return assumptions. The NaviPlan software also models five bond asset classes, but not inflation-protected bonds, even though Treasury Inflation Protected Bonds have been available for more than ten years.
The NaviPlan software also generates charts that show the distribution of excess or overall wealth generated by the simulation. These projections, however, do not appear to factor in the likelihood that a retiree would increase their retirement expenditures if they enjoyed a stock market windfall. These modeling assumptions do not reflect typical human behavioral responses to abundance (which is to consume more) and scarcity (which is to conserve more). Overall, the NaviPlan software's modeling assumptions—modeling returns with a stationary normal distribution centered about average historical returns, and modeling expenditures as if they were relatively constant—greatly limit the usefulness of NaviPlan's Monte Carlo outputs in helping an investor select a reasonable asset allocation.
The Financeware software also uses a stationary normal distribution approximately centered around historical return rates to project future equity return rates. Online documentation indicates that as of August 2002, the Financeware software modeled over a dozen different equity asset classes on the presumption that they would return, on average, between 12% and 21% per year. The Financeware software only recently—and after the date of the present invention—added Treasury Inflation Protected Bonds as an asset class. The Financeware software did not, as of 2008, evaluate the risks and rewards of a mixed portfolio as a function of the expected performance an all-TIPS portfolio.
As of 2008, Money Guide Pro provided both Monte Carlo and exploratory simulation modes. Money Guide Pro's Monte Carlo simulation mode uses historical return rates to project future equity return rates. Money Guide Pro's exploratory simulation mode also uses actual—not mean-adjusted—historical returns and inflation rates in sequence. The Money Guide Pro software does not, as of 2008, model Treasury Inflation Protected Bonds as an asset class.
Applicant has also investigated ESPlannerPLUS by Economic Security Inc. of Lexington, Mass. ESPlannerPLUS provides Monte Carlo simulation centered around a user-specified expected nominal arithmetic mean return. Online documentation suggests that ESPlannerPLUS models a person's spending by computing the maximum sustainable consumption the person's lifetime of savings would support if the person's equities grew at a constant rate of return equal to the expected nominal arithmetic mean return. ESPlannerPLUS's online documentation admits that this is an extremely aggressive assumption. And its online tutorials suggest that this assumption leads to extremely dismal success rates.
Finally, Applicant has investigated J&L Financial Planner by J&L Software, LLC, of Taneytown, Md. J&L Financial Planner provides two “Historical Return Analysis” modes. The first is a form of exploratory simulation. According to its online documentation, this mode “generates your net worth for each year of your plan based on the returns of the historical data starting with the first year of the data.” The second is a form of Monte Carlo simulation. According to its online documentation, this mode tests a financial plan and portfolio against randomly selected historical return data. But there is no indication in the documentation indicating that the historical return data is scaled to yield the user's expected return. J&L Financial Planner also provides more traditional Monte Carlo simulation, but like its peers simulates only with stationary distributions, modeling every simulated return as if it were independent of all of the other simulated returns.